Overview

Dataset statistics

Number of variables17
Number of observations1143
Missing cells12348
Missing cells (%)63.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory151.9 KiB
Average record size in memory136.1 B

Variable types

Categorical2
TimeSeries15

Alerts

inflation_expectations_CHL_CB has constant value ""Constant
Date has a high cardinality: 1143 distinct valuesHigh cardinality
chile_interest_rates_CHL_CB is highly overall correlated with headline_cpi_yoy_CHL_CB and 2 other fieldsHigh correlation
china_inflation_CHL_CB is highly overall correlated with daily_retail_sales_CHL_CB and 2 other fieldsHigh correlation
daily_retail_sales_CHL_CB is highly overall correlated with china_inflation_CHL_CB and 2 other fieldsHigh correlation
eurozone_inflation_CHL_CB is highly overall correlated with china_inflation_CHL_CB and 1 other fieldsHigh correlation
gdp_chained_volume_CHL_CB is highly overall correlated with imacec_monthly_activity_CHL_CB and 4 other fieldsHigh correlation
headline_cpi_yoy_CHL_CB is highly overall correlated with chile_interest_rates_CHL_CB and 4 other fieldsHigh correlation
imacec_monthly_activity_CHL_CB is highly overall correlated with gdp_chained_volume_CHL_CB and 4 other fieldsHigh correlation
m1_money_supply_CHL_CB is highly overall correlated with daily_retail_sales_CHL_CB and 6 other fieldsHigh correlation
m2_money_supply_CHL_CB is highly overall correlated with gdp_chained_volume_CHL_CB and 5 other fieldsHigh correlation
m3_money_supply_CHL_CB is highly overall correlated with gdp_chained_volume_CHL_CB and 5 other fieldsHigh correlation
oil_prices_CHL_CB is highly overall correlated with real_earning_index_(wages)_CHL_CB and 1 other fieldsHigh correlation
real_earning_index_(wages)_CHL_CB is highly overall correlated with chile_interest_rates_CHL_CB and 7 other fieldsHigh correlation
unemployment_rate_CHL_CB is highly overall correlated with chile_interest_rates_CHL_CB and 1 other fieldsHigh correlation
us_inflation_CHL_CB is highly overall correlated with china_inflation_CHL_CB and 2 other fieldsHigh correlation
chile_interest_rates_CHL_CB has 807 (70.6%) missing valuesMissing
china_inflation_CHL_CB has 780 (68.2%) missing valuesMissing
daily_retail_sales_CHL_CB has 1092 (95.5%) missing valuesMissing
eurozone_inflation_CHL_CB has 744 (65.1%) missing valuesMissing
gdp_chained_volume_CHL_CB has 1035 (90.6%) missing valuesMissing
imacec_monthly_activity_CHL_CB has 816 (71.4%) missing valuesMissing
inflation_expectations_CHL_CB has 1128 (98.7%) missing valuesMissing
m1_money_supply_CHL_CB has 454 (39.7%) missing valuesMissing
m2_money_supply_CHL_CB has 695 (60.8%) missing valuesMissing
m3_money_supply_CHL_CB has 695 (60.8%) missing valuesMissing
oil_prices_CHL_CB has 863 (75.5%) missing valuesMissing
real_earning_index_(wages)_CHL_CB has 942 (82.4%) missing valuesMissing
real_exchange_rate_CHL_CB has 696 (60.9%) missing valuesMissing
unemployment_rate_CHL_CB has 854 (74.7%) missing valuesMissing
us_inflation_CHL_CB has 744 (65.1%) missing valuesMissing
chile_interest_rates_CHL_CB is non stationaryNon stationary
daily_retail_sales_CHL_CB is non stationaryNon stationary
gdp_chained_volume_CHL_CB is non stationaryNon stationary
imacec_monthly_activity_CHL_CB is non stationaryNon stationary
m1_money_supply_CHL_CB is non stationaryNon stationary
m2_money_supply_CHL_CB is non stationaryNon stationary
m3_money_supply_CHL_CB is non stationaryNon stationary
oil_prices_CHL_CB is non stationaryNon stationary
real_earning_index_(wages)_CHL_CB is non stationaryNon stationary
unemployment_rate_CHL_CB is non stationaryNon stationary
Date is uniformly distributedUniform
Date has unique valuesUnique
headline_cpi_yoy_CHL_CB has 25 (2.2%) zerosZeros

Reproduction

Analysis started2023-05-22 15:23:31.056571
Analysis finished2023-05-22 15:24:10.565254
Duration39.51 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Date
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct1143
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.1 KiB
1928-03-01
 
1
1991-07-01
 
1
1992-01-01
 
1
1991-12-01
 
1
1991-11-01
 
1
Other values (1138)
1138 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters11430
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1143 ?
Unique (%)100.0%

Sample

1st row1928-03-01
2nd row1928-04-01
3rd row1928-05-01
4th row1928-06-01
5th row1928-07-01

Common Values

ValueCountFrequency (%)
1928-03-01 1
 
0.1%
1991-07-01 1
 
0.1%
1992-01-01 1
 
0.1%
1991-12-01 1
 
0.1%
1991-11-01 1
 
0.1%
1991-10-01 1
 
0.1%
1991-09-01 1
 
0.1%
1991-08-01 1
 
0.1%
1991-06-01 1
 
0.1%
1992-03-01 1
 
0.1%
Other values (1133) 1133
99.1%

Length

2023-05-22T11:24:10.668611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1928-03-01 1
 
0.1%
1929-08-01 1
 
0.1%
1928-05-01 1
 
0.1%
1928-06-01 1
 
0.1%
1928-07-01 1
 
0.1%
1928-08-01 1
 
0.1%
1928-09-01 1
 
0.1%
1928-10-01 1
 
0.1%
1928-11-01 1
 
0.1%
1929-09-01 1
 
0.1%
Other values (1133) 1133
99.1%

Most occurring characters

ValueCountFrequency (%)
1 2720
23.8%
0 2617
22.9%
- 2286
20.0%
9 1197
10.5%
2 654
 
5.7%
8 333
 
2.9%
3 329
 
2.9%
5 324
 
2.8%
4 324
 
2.8%
7 323
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9144
80.0%
Dash Punctuation 2286
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2720
29.7%
0 2617
28.6%
9 1197
13.1%
2 654
 
7.2%
8 333
 
3.6%
3 329
 
3.6%
5 324
 
3.5%
4 324
 
3.5%
7 323
 
3.5%
6 323
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 2286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2720
23.8%
0 2617
22.9%
- 2286
20.0%
9 1197
10.5%
2 654
 
5.7%
8 333
 
2.9%
3 329
 
2.9%
5 324
 
2.8%
4 324
 
2.8%
7 323
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2720
23.8%
0 2617
22.9%
- 2286
20.0%
9 1197
10.5%
2 654
 
5.7%
8 333
 
2.9%
3 329
 
2.9%
5 324
 
2.8%
4 324
 
2.8%
7 323
 
2.8%

chile_interest_rates_CHL_CB
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY 

Distinct134
Distinct (%)39.9%
Missing807
Missing (%)70.6%
Infinite0
Infinite (%)0.0%
Mean4.4048647
Minimum0.5
Maximum12.761905
Zeros0
Zeros (%)0.0%
Memory size9.1 KiB
2023-05-22T11:24:10.816198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q12.75
median4.35
Q35.75
95-th percentile8.5
Maximum12.761905
Range12.261905
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4379397
Coefficient of variation (CV)0.55346529
Kurtosis0.41524585
Mean4.4048647
Median Absolute Deviation (MAD)1.6
Skewness0.61852278
Sum1480.0345
Variance5.94355
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.1344490526
2023-05-22T11:24:10.998440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 37
 
3.2%
0.5 25
 
2.2%
3 19
 
1.7%
2.5 18
 
1.6%
2.75 14
 
1.2%
3.5 14
 
1.2%
5.25 12
 
1.0%
1.75 11
 
1.0%
7.5 10
 
0.9%
6.5 9
 
0.8%
Other values (124) 167
 
14.6%
(Missing) 807
70.6%
ValueCountFrequency (%)
0.5 25
2.2%
0.58 1
 
0.1%
0.63 1
 
0.1%
0.74 1
 
0.1%
0.75 1
 
0.1%
1.04 1
 
0.1%
1.24 1
 
0.1%
1.36 1
 
0.1%
1.38 1
 
0.1%
1.5 1
 
0.1%
ValueCountFrequency (%)
12.76190476 1
 
0.1%
11.25 6
0.5%
11.07 1
 
0.1%
10.975 1
 
0.1%
10.55 1
 
0.1%
9.80952381 1
 
0.1%
9.75 1
 
0.1%
9.43 1
 
0.1%
8.81 1
 
0.1%
8.5 6
0.5%
2023-05-22T11:24:11.311140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

china_inflation_CHL_CB
Numeric time series

HIGH CORRELATION  MISSING 

Distinct172
Distinct (%)47.4%
Missing780
Missing (%)68.2%
Infinite0
Infinite (%)0.0%
Mean3.0134986
Minimum-8.2
Maximum26
Zeros4
Zeros (%)0.3%
Memory size9.1 KiB
2023-05-22T11:24:11.625749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-8.2
5-th percentile-4.99
Q1-1.6
median1.9
Q35.95
95-th percentile19.05
Maximum26
Range34.2
Interquartile range (IQR)7.55

Descriptive statistics

Standard deviation6.8032215
Coefficient of variation (CV)2.2575824
Kurtosis1.6932423
Mean3.0134986
Median Absolute Deviation (MAD)3.8
Skewness1.2851347
Sum1093.9
Variance46.283823
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.004467685526
2023-05-22T11:24:11.902038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.4 7
 
0.6%
0.1 7
 
0.6%
-2.1 5
 
0.4%
3.5 5
 
0.4%
-1.3 5
 
0.4%
2.7 5
 
0.4%
6.4 5
 
0.4%
6.8 5
 
0.4%
-1.6 5
 
0.4%
-5.9 5
 
0.4%
Other values (162) 309
 
27.0%
(Missing) 780
68.2%
ValueCountFrequency (%)
-8.2 1
 
0.1%
-7.9 1
 
0.1%
-7.8 1
 
0.1%
-7.2 1
 
0.1%
-7 1
 
0.1%
-6.6 1
 
0.1%
-6 1
 
0.1%
-5.9 5
0.4%
-5.8 1
 
0.1%
-5.7 1
 
0.1%
ValueCountFrequency (%)
26 1
0.1%
25.9 1
0.1%
25.7 1
0.1%
25.3 1
0.1%
24.6 1
0.1%
24.1 1
0.1%
22.5 1
0.1%
22.3 1
0.1%
22 1
0.1%
21.3 1
0.1%
2023-05-22T11:24:12.647694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

daily_retail_sales_CHL_CB
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY 

Distinct51
Distinct (%)100.0%
Missing1092
Missing (%)95.5%
Infinite0
Infinite (%)0.0%
Mean9.6921721
Minimum-27.548587
Maximum79.067941
Zeros0
Zeros (%)0.0%
Memory size9.1 KiB
2023-05-22T11:24:13.145262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-27.548587
5-th percentile-19.331194
Q1-4.5695405
median3.1158836
Q321.451706
95-th percentile57.564785
Maximum79.067941
Range106.61653
Interquartile range (IQR)26.021246

Descriptive statistics

Standard deviation22.855268
Coefficient of variation (CV)2.3581162
Kurtosis2.2994994
Mean9.6921721
Median Absolute Deviation (MAD)11.797439
Skewness1.3197014
Sum494.30077
Variance522.36327
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.2433094683
2023-05-22T11:24:13.380377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5373735091 1
 
0.1%
42.20354436 1
 
0.1%
79.06794061 1
 
0.1%
72.92602663 1
 
0.1%
75.37014926 1
 
0.1%
22.86279243 1
 
0.1%
23.83480921 1
 
0.1%
32.49997754 1
 
0.1%
21.91236804 1
 
0.1%
17.83070466 1
 
0.1%
Other values (41) 41
 
3.6%
(Missing) 1092
95.5%
ValueCountFrequency (%)
-27.54858714 1
0.1%
-26.20085501 1
0.1%
-22.46357571 1
0.1%
-16.19881187 1
0.1%
-9.509134683 1
0.1%
-9.108996557 1
0.1%
-8.681555813 1
0.1%
-8.407299069 1
0.1%
-7.282983561 1
0.1%
-7.145646865 1
0.1%
ValueCountFrequency (%)
79.06794061 1
0.1%
75.37014926 1
0.1%
72.92602663 1
0.1%
42.20354436 1
0.1%
37.75394613 1
0.1%
32.99772136 1
0.1%
32.49997754 1
0.1%
23.83480921 1
0.1%
23.47004057 1
0.1%
22.96342265 1
0.1%
2023-05-22T11:24:13.544924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

eurozone_inflation_CHL_CB
Numeric time series

HIGH CORRELATION  MISSING 

Distinct133
Distinct (%)33.3%
Missing744
Missing (%)65.1%
Infinite0
Infinite (%)0.0%
Mean2.8220551
Minimum-8.2
Maximum43.4
Zeros4
Zeros (%)0.3%
Memory size9.1 KiB
2023-05-22T11:24:13.861319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-8.2
5-th percentile-3
Q1-0.4
median1.8
Q33.9
95-th percentile10.69
Maximum43.4
Range51.6
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation6.7403245
Coefficient of variation (CV)2.3884454
Kurtosis15.537504
Mean2.8220551
Median Absolute Deviation (MAD)2.1
Skewness3.5924703
Sum1126
Variance45.431975
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.226358376 × 10-6
2023-05-22T11:24:14.025831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.6 11
 
1.0%
1.5 11
 
1.0%
1 10
 
0.9%
4.3 10
 
0.9%
1.3 9
 
0.8%
1.1 9
 
0.8%
-0.5 8
 
0.7%
2.2 8
 
0.7%
2.5 8
 
0.7%
2 8
 
0.7%
Other values (123) 307
26.9%
(Missing) 744
65.1%
ValueCountFrequency (%)
-8.2 1
0.1%
-7.5 1
0.1%
-7.2 1
0.1%
-6.4 1
0.1%
-6.3 1
0.1%
-5.7 1
0.1%
-5 1
0.1%
-4.6 1
0.1%
-4.5 1
0.1%
-4.3 1
0.1%
ValueCountFrequency (%)
43.4 1
0.1%
41.8 1
0.1%
38 1
0.1%
37.2 1
0.1%
36.9 1
0.1%
36.1 2
0.2%
31.5 1
0.1%
30.7 1
0.1%
30.4 1
0.1%
26.9 1
0.1%
2023-05-22T11:24:14.257614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

gdp_chained_volume_CHL_CB
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY 

Distinct108
Distinct (%)100.0%
Missing1035
Missing (%)90.6%
Infinite0
Infinite (%)0.0%
Mean35539.876
Minimum19581.249
Maximum54840.394
Zeros0
Zeros (%)0.0%
Memory size9.1 KiB
2023-05-22T11:24:14.558475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum19581.249
5-th percentile21538.028
Q125693.865
median35504.99
Q344385.702
95-th percentile49423.897
Maximum54840.394
Range35259.146
Interquartile range (IQR)18691.837

Descriptive statistics

Standard deviation9805.0404
Coefficient of variation (CV)0.27588843
Kurtosis-1.3268715
Mean35539.876
Median Absolute Deviation (MAD)9233.0385
Skewness0.015214021
Sum3838306.6
Variance96138818
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.8626938972
2023-05-22T11:24:14.708633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41321.58076 1
 
0.1%
46406.79026 1
 
0.1%
42486.52481 1
 
0.1%
44343.76707 1
 
0.1%
43392.76861 1
 
0.1%
45575.77245 1
 
0.1%
41541.38014 1
 
0.1%
43459.58846 1
 
0.1%
42332.20853 1
 
0.1%
44804.09131 1
 
0.1%
Other values (98) 98
 
8.6%
(Missing) 1035
90.6%
ValueCountFrequency (%)
19581.24855 1
0.1%
20264.76288 1
0.1%
20368.67568 1
0.1%
21177.77624 1
0.1%
21401.29791 1
0.1%
21420.8115 1
0.1%
21755.71522 1
0.1%
21856.56818 1
0.1%
22010.46453 1
0.1%
22457.01443 1
0.1%
ValueCountFrequency (%)
54840.39406 1
0.1%
53588.91391 1
0.1%
51360.88537 1
0.1%
50613.1509 1
0.1%
50065.32781 1
0.1%
49459.58171 1
0.1%
49357.6246 1
0.1%
48844.12544 1
0.1%
48749.00093 1
0.1%
48666.06087 1
0.1%
2023-05-22T11:24:14.863595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

headline_cpi_yoy_CHL_CB
Numeric time series

HIGH CORRELATION  ZEROS 

Distinct480
Distinct (%)42.1%
Missing3
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean32.646316
Minimum-7.5
Maximum746.3
Zeros25
Zeros (%)2.2%
Memory size9.1 KiB
2023-05-22T11:24:15.124941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-7.5
5-th percentile4.8819121 × 10-7
Q13.8
median13.2
Q326.6
95-th percentile93.135
Maximum746.3
Range753.8
Interquartile range (IQR)22.8

Descriptive statistics

Standard deviation82.109688
Coefficient of variation (CV)2.5151288
Kurtosis34.066241
Mean32.646316
Median Absolute Deviation (MAD)10.1
Skewness5.506757
Sum37216.8
Variance6742.0009
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.000135687494
2023-05-22T11:24:15.285517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
 
2.2%
3.8 13
 
1.1%
2.7 13
 
1.1%
2.8 13
 
1.1%
2.6 12
 
1.0%
1.9 12
 
1.0%
2.9 11
 
1.0%
3 10
 
0.9%
2.5 10
 
0.9%
4.5 9
 
0.8%
Other values (470) 1012
88.5%
ValueCountFrequency (%)
-7.5 1
 
0.1%
-5.2 2
 
0.2%
-3.9 1
 
0.1%
-3.8 1
 
0.1%
-3.6 2
 
0.2%
-2.7 2
 
0.2%
-2.3 1
 
0.1%
-1.9 4
0.3%
-1.8 3
0.3%
-1.4 5
0.4%
ValueCountFrequency (%)
746.3 1
0.1%
708.8 1
0.1%
704.6 1
0.1%
678.2 1
0.1%
670.2 1
0.1%
651.9 1
0.1%
637.2 1
0.1%
611.5 1
0.1%
529.1 1
0.1%
528.9 1
0.1%
2023-05-22T11:24:15.639229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

imacec_monthly_activity_CHL_CB
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY 

Distinct327
Distinct (%)100.0%
Missing816
Missing (%)71.4%
Infinite0
Infinite (%)0.0%
Mean75.291914
Minimum42.005682
Maximum110.43318
Zeros0
Zeros (%)0.0%
Memory size9.1 KiB
2023-05-22T11:24:15.954733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum42.005682
5-th percentile45.964893
Q154.611088
median73.67687
Q393.739161
95-th percentile107.16272
Maximum110.43318
Range68.427497
Interquartile range (IQR)39.128073

Descriptive statistics

Standard deviation20.616081
Coefficient of variation (CV)0.27381534
Kurtosis-1.370727
Mean75.291914
Median Absolute Deviation (MAD)19.900642
Skewness-0.015680875
Sum24620.456
Variance425.0228
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.8930036738
2023-05-22T11:24:16.109382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90.89940452 1
 
0.1%
90.63327284 1
 
0.1%
90.94244072 1
 
0.1%
91.41684889 1
 
0.1%
91.32799788 1
 
0.1%
90.77426146 1
 
0.1%
90.75833367 1
 
0.1%
90.04826549 1
 
0.1%
89.86778399 1
 
0.1%
90.56785215 1
 
0.1%
Other values (317) 317
 
27.7%
(Missing) 816
71.4%
ValueCountFrequency (%)
42.00568205 1
0.1%
42.50997692 1
0.1%
42.51195926 1
0.1%
42.55210001 1
0.1%
42.64422697 1
0.1%
43.09513573 1
0.1%
43.10127125 1
0.1%
43.21291333 1
0.1%
43.25623399 1
0.1%
43.69817969 1
0.1%
ValueCountFrequency (%)
110.4331795 1
0.1%
110.3460473 1
0.1%
109.9442462 1
0.1%
109.218388 1
0.1%
109.1673593 1
0.1%
109.0725518 1
0.1%
109.0468106 1
0.1%
108.8131366 1
0.1%
108.7279446 1
0.1%
108.5918787 1
0.1%
2023-05-22T11:24:16.455552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

inflation_expectations_CHL_CB
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)6.7%
Missing1128
Missing (%)98.7%
Memory size9.1 KiB
3.0
15 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 15
 
1.3%
(Missing) 1128
98.7%

Length

2023-05-22T11:24:16.769802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-22T11:24:16.900461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3.0 15
100.0%

Most occurring characters

ValueCountFrequency (%)
3 15
33.3%
. 15
33.3%
0 15
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30
66.7%
Other Punctuation 15
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 15
50.0%
0 15
50.0%
Other Punctuation
ValueCountFrequency (%)
. 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 15
33.3%
. 15
33.3%
0 15
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 15
33.3%
. 15
33.3%
0 15
33.3%

m1_money_supply_CHL_CB
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY 

Distinct689
Distinct (%)100.0%
Missing454
Missing (%)39.7%
Infinite0
Infinite (%)0.0%
Mean9584.3116
Minimum0.0019602846
Maximum80304.795
Zeros0
Zeros (%)0.0%
Memory size9.1 KiB
2023-05-22T11:24:17.006865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.0019602846
5-th percentile0.0043832529
Q169.620185
median1629.28
Q310535.2
95-th percentile50582.711
Maximum80304.795
Range80304.793
Interquartile range (IQR)10465.58

Descriptive statistics

Standard deviation16709.068
Coefficient of variation (CV)1.743377
Kurtosis5.528462
Mean9584.3116
Median Absolute Deviation (MAD)1629.2568
Skewness2.3772416
Sum6603590.7
Variance2.7919297 × 108
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1
2023-05-22T11:24:17.165787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6088.11 1
 
0.1%
4919.82 1
 
0.1%
5008.82 1
 
0.1%
5492.03 1
 
0.1%
5654.42 1
 
0.1%
5591.28 1
 
0.1%
5951.71 1
 
0.1%
5715.71 1
 
0.1%
5981.1 1
 
0.1%
6073.69 1
 
0.1%
Other values (679) 679
59.4%
(Missing) 454
39.7%
ValueCountFrequency (%)
0.001960284607 1
0.1%
0.002051314637 1
0.1%
0.002112001323 1
0.1%
0.00222081883 1
0.1%
0.002314987827 1
0.1%
0.002340099559 1
0.1%
0.00247978357 1
0.1%
0.002582323144 1
0.1%
0.002602726427 1
0.1%
0.002692186973 1
0.1%
ValueCountFrequency (%)
80304.79465 1
0.1%
80282.65794 1
0.1%
79993.19183 1
0.1%
79859.34359 1
0.1%
79767.35235 1
0.1%
79243.54479 1
0.1%
78677.90252 1
0.1%
77008.42238 1
0.1%
76869.66203 1
0.1%
73610.65779 1
0.1%
2023-05-22T11:24:17.441421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

m2_money_supply_CHL_CB
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY 

Distinct448
Distinct (%)100.0%
Missing695
Missing (%)60.8%
Infinite0
Infinite (%)0.0%
Mean52959.333
Minimum753.67
Maximum188783.96
Zeros0
Zeros (%)0.0%
Memory size9.1 KiB
2023-05-22T11:24:17.733143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum753.67
5-th percentile1315.4485
Q18860.225
median25250.055
Q391285.748
95-th percentile165976.76
Maximum188783.96
Range188030.29
Interquartile range (IQR)82425.523

Descriptive statistics

Standard deviation54271.471
Coefficient of variation (CV)1.0247763
Kurtosis-0.32233174
Mean52959.333
Median Absolute Deviation (MAD)23266.575
Skewness0.9718855
Sum23725781
Variance2.9453926 × 109
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1
2023-05-22T11:24:17.897873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25594.12 1
 
0.1%
65635.2875 1
 
0.1%
65132.404 1
 
0.1%
63985.5525 1
 
0.1%
62565.236 1
 
0.1%
61286.124 1
 
0.1%
60641.1365 1
 
0.1%
60852.053 1
 
0.1%
60000.3645 1
 
0.1%
58705.7395 1
 
0.1%
Other values (438) 438
38.3%
(Missing) 695
60.8%
ValueCountFrequency (%)
753.67 1
0.1%
778.18 1
0.1%
792.86 1
0.1%
806.66 1
0.1%
819.9 1
0.1%
837.47 1
0.1%
859.19 1
0.1%
876.7 1
0.1%
891.26 1
0.1%
905.93 1
0.1%
ValueCountFrequency (%)
188783.9558 1
0.1%
187269.7152 1
0.1%
184464.4534 1
0.1%
183693.3577 1
0.1%
183319.591 1
0.1%
181438.8809 1
0.1%
181072.6285 1
0.1%
180477.3103 1
0.1%
178654.7568 1
0.1%
178208.2783 1
0.1%
2023-05-22T11:24:18.150000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

m3_money_supply_CHL_CB
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY 

Distinct448
Distinct (%)100.0%
Missing695
Missing (%)60.8%
Infinite0
Infinite (%)0.0%
Mean90701.757
Minimum1231.79
Maximum319009.99
Zeros0
Zeros (%)0.0%
Memory size9.1 KiB
2023-05-22T11:24:19.162400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1231.79
5-th percentile2201.539
Q117993.685
median50007.195
Q3147583.67
95-th percentile279352.74
Maximum319009.99
Range317778.2
Interquartile range (IQR)129589.99

Descriptive statistics

Standard deviation91144.249
Coefficient of variation (CV)1.0048785
Kurtosis-0.17995102
Mean90701.757
Median Absolute Deviation (MAD)42574.736
Skewness1.0169831
Sum40634387
Variance8.3072741 × 109
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1
2023-05-22T11:24:19.336054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50388.84 1
 
0.1%
109988.4875 1
 
0.1%
108756.904 1
 
0.1%
107005.4525 1
 
0.1%
105261.336 1
 
0.1%
103823.424 1
 
0.1%
103135.3365 1
 
0.1%
102276.453 1
 
0.1%
100198.2645 1
 
0.1%
98470.9395 1
 
0.1%
Other values (438) 438
38.3%
(Missing) 695
60.8%
ValueCountFrequency (%)
1231.79 1
0.1%
1265.63 1
0.1%
1310.2 1
0.1%
1352.1 1
0.1%
1365.9 1
0.1%
1391.5 1
0.1%
1413.49 1
0.1%
1444.22 1
0.1%
1477.44 1
0.1%
1507.49 1
0.1%
ValueCountFrequency (%)
319009.991 1
0.1%
316633.9316 1
0.1%
313834.836 1
0.1%
313622.4183 1
0.1%
313602.0144 1
0.1%
313477.2261 1
0.1%
313310.1893 1
0.1%
313092.7719 1
0.1%
312922.6613 1
0.1%
311942.2091 1
0.1%
2023-05-22T11:24:19.643037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

oil_prices_CHL_CB
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY 

Distinct276
Distinct (%)98.6%
Missing863
Missing (%)75.5%
Infinite0
Infinite (%)0.0%
Mean65.708786
Minimum18.6
Maximum133.9
Zeros0
Zeros (%)0.0%
Memory size9.1 KiB
2023-05-22T11:24:19.953044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum18.6
5-th percentile25.3865
Q143.045
median62.875
Q385.1175
95-th percentile113.7645
Maximum133.9
Range115.3
Interquartile range (IQR)42.0725

Descriptive statistics

Standard deviation29.411588
Coefficient of variation (CV)0.44760511
Kurtosis-0.92594126
Mean65.708786
Median Absolute Deviation (MAD)20.7
Skewness0.32242863
Sum18398.46
Variance865.04152
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.1029344466
2023-05-22T11:24:20.096226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74.31 2
 
0.2%
27.55 2
 
0.2%
62.33 2
 
0.2%
25.64 2
 
0.2%
62.34 1
 
0.1%
48.12 1
 
0.1%
47.24 1
 
0.1%
46.99 1
 
0.1%
55.87 1
 
0.1%
59.39 1
 
0.1%
Other values (266) 266
 
23.3%
(Missing) 863
75.5%
ValueCountFrequency (%)
18.6 1
0.1%
18.94 1
0.1%
19.49 1
0.1%
20.29 1
0.1%
20.48 1
0.1%
22.54 1
0.1%
23.34 1
0.1%
23.69 1
0.1%
24.13 1
0.1%
24.18 1
0.1%
ValueCountFrequency (%)
133.9 1
0.1%
133.05 1
0.1%
124.93 1
0.1%
123.94 1
0.1%
123.04 1
0.1%
120.46 1
0.1%
120.08 1
0.1%
119.7 1
0.1%
116.46 1
0.1%
116.45 1
0.1%
2023-05-22T11:24:20.864470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

real_earning_index_(wages)_CHL_CB
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY 

Distinct195
Distinct (%)97.0%
Missing942
Missing (%)82.4%
Infinite0
Infinite (%)0.0%
Mean91.818458
Minimum70.59
Maximum111.29
Zeros0
Zeros (%)0.0%
Memory size9.1 KiB
2023-05-22T11:24:21.302971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum70.59
5-th percentile75.39
Q185.17
median92.2
Q398.92
95-th percentile106.39
Maximum111.29
Range40.7
Interquartile range (IQR)13.75

Descriptive statistics

Standard deviation9.5839755
Coefficient of variation (CV)0.10437962
Kurtosis-0.59229548
Mean91.818458
Median Absolute Deviation (MAD)6.98
Skewness-0.29374299
Sum18455.51
Variance91.852587
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9481881382
2023-05-22T11:24:21.475841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.14 2
 
0.2%
98.49 2
 
0.2%
103.76 2
 
0.2%
90.76 2
 
0.2%
79.84 2
 
0.2%
92.03 2
 
0.2%
97.56 1
 
0.1%
97.42 1
 
0.1%
94.32 1
 
0.1%
97.14 1
 
0.1%
Other values (185) 185
 
16.2%
(Missing) 942
82.4%
ValueCountFrequency (%)
70.59 1
0.1%
70.74 1
0.1%
70.85 1
0.1%
71.2 1
0.1%
71.24 1
0.1%
71.48 1
0.1%
71.54 1
0.1%
71.85 1
0.1%
74.9 1
0.1%
75.02 1
0.1%
ValueCountFrequency (%)
111.29 1
0.1%
109.78 1
0.1%
109.2 1
0.1%
108.94 1
0.1%
108.71 1
0.1%
108.37 1
0.1%
107.49 1
0.1%
107.38 1
0.1%
106.9 1
0.1%
106.45 1
0.1%
2023-05-22T11:24:22.420684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

real_exchange_rate_CHL_CB
Numeric time series

Distinct447
Distinct (%)100.0%
Missing696
Missing (%)60.9%
Infinite0
Infinite (%)0.0%
Mean95.847342
Minimum75.265231
Maximum119.77844
Zeros0
Zeros (%)0.0%
Memory size9.1 KiB
2023-05-22T11:24:22.740348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum75.265231
5-th percentile78.927202
Q190.693088
median95.087706
Q3101.17531
95-th percentile111.36545
Maximum119.77844
Range44.513213
Interquartile range (IQR)10.482227

Descriptive statistics

Standard deviation8.8644244
Coefficient of variation (CV)0.092484822
Kurtosis-0.21323788
Mean95.847342
Median Absolute Deviation (MAD)5.3119235
Skewness0.066135728
Sum42843.762
Variance78.57802
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.04261655558
2023-05-22T11:24:22.923640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.65405489 1
 
0.1%
91.01659815 1
 
0.1%
90.5220946 1
 
0.1%
91.28855116 1
 
0.1%
91.33766123 1
 
0.1%
90.01477011 1
 
0.1%
91.46922808 1
 
0.1%
87.40232777 1
 
0.1%
88.91231385 1
 
0.1%
89.0730835 1
 
0.1%
Other values (437) 437
38.2%
(Missing) 696
60.9%
ValueCountFrequency (%)
75.26523131 1
0.1%
76.06869802 1
0.1%
76.53447047 1
0.1%
76.62270803 1
0.1%
76.97026414 1
0.1%
77.09648851 1
0.1%
77.56109103 1
0.1%
77.61080685 1
0.1%
77.72323618 1
0.1%
77.75899402 1
0.1%
ValueCountFrequency (%)
119.7784446 1
0.1%
115.9017167 1
0.1%
115.6877254 1
0.1%
115.2763854 1
0.1%
115.1852257 1
0.1%
115.1482405 1
0.1%
114.9650848 1
0.1%
114.2564674 1
0.1%
113.8832674 1
0.1%
113.3978336 1
0.1%
2023-05-22T11:24:23.166477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

unemployment_rate_CHL_CB
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY 

Distinct287
Distinct (%)99.3%
Missing854
Missing (%)74.7%
Infinite0
Infinite (%)0.0%
Mean8.523521
Minimum5.121823
Maximum13.545649
Zeros0
Zeros (%)0.0%
Memory size9.1 KiB
2023-05-22T11:24:23.498320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5.121823
5-th percentile6.0758816
Q17.0754707
median8.3796407
Q39.9129934
95-th percentile11.597554
Maximum13.545649
Range8.423826
Interquartile range (IQR)2.8375227

Descriptive statistics

Standard deviation1.7657726
Coefficient of variation (CV)0.20716469
Kurtosis-0.52465442
Mean8.523521
Median Absolute Deviation (MAD)1.369658
Skewness0.37814458
Sum2463.2976
Variance3.117953
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.05552846532
2023-05-22T11:24:23.669666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.8 2
 
0.2%
10.2 2
 
0.2%
10.42009914 1
 
0.1%
9.631665684 1
 
0.1%
9.601495596 1
 
0.1%
9.103608622 1
 
0.1%
8.783655237 1
 
0.1%
8.642775737 1
 
0.1%
9.616050018 1
 
0.1%
10.48480818 1
 
0.1%
Other values (277) 277
 
24.2%
(Missing) 854
74.7%
ValueCountFrequency (%)
5.121822978 1
0.1%
5.248044708 1
0.1%
5.29875791 1
0.1%
5.303764886 1
0.1%
5.341996506 1
0.1%
5.39186453 1
0.1%
5.437321892 1
0.1%
5.440257585 1
0.1%
5.666321647 1
0.1%
5.835819611 1
0.1%
ValueCountFrequency (%)
13.54564895 1
0.1%
13.35240296 1
0.1%
12.6603895 1
0.1%
12.57401331 1
0.1%
12.54888572 1
0.1%
12.41498447 1
0.1%
12.2069159 1
0.1%
12.20659535 1
0.1%
12.13695412 1
0.1%
11.96737029 1
0.1%
2023-05-22T11:24:23.944764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

us_inflation_CHL_CB
Numeric time series

HIGH CORRELATION  MISSING 

Distinct346
Distinct (%)86.7%
Missing744
Missing (%)65.1%
Infinite0
Infinite (%)0.0%
Mean2.7988722
Minimum-16.06
Maximum22.69
Zeros0
Zeros (%)0.0%
Memory size9.1 KiB
2023-05-22T11:24:24.222709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-16.06
5-th percentile-6.573
Q1-0.335
median2.24
Q35.555
95-th percentile13.839
Maximum22.69
Range38.75
Interquartile range (IQR)5.89

Descriptive statistics

Standard deviation6.0107943
Coefficient of variation (CV)2.1475773
Kurtosis1.9570133
Mean2.7988722
Median Absolute Deviation (MAD)3.1
Skewness0.6606094
Sum1116.75
Variance36.129648
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.993018956 × 10-6
2023-05-22T11:24:24.392087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.85 4
 
0.3%
2.15 4
 
0.3%
6.72 3
 
0.3%
0.34 3
 
0.3%
2.56 3
 
0.3%
4.51 2
 
0.2%
1.27 2
 
0.2%
4.07 2
 
0.2%
7.04 2
 
0.2%
0.84 2
 
0.2%
Other values (336) 372
32.5%
(Missing) 744
65.1%
ValueCountFrequency (%)
-16.06 1
0.1%
-13.17 1
0.1%
-13.12 1
0.1%
-12.06 1
0.1%
-11.58 1
0.1%
-11.42 1
0.1%
-10.54 1
0.1%
-8.38 1
0.1%
-8.35 1
0.1%
-8.21 2
0.2%
ValueCountFrequency (%)
22.69 1
0.1%
22.43 1
0.1%
22.37 1
0.1%
21.76 1
0.1%
21.5 1
0.1%
20.94 1
0.1%
20.55 1
0.1%
20.37 1
0.1%
20.34 1
0.1%
20.13 2
0.2%
2023-05-22T11:24:24.746836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ACF and PACF

Interactions

2023-05-22T11:24:06.622598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:31.928149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:34.217032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:36.513776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:39.850883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:42.175599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:44.544522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:47.004485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:49.963135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:52.524334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:55.040710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:57.268063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:59.512582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:02.408536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:04.510942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:06.767095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:32.079490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:34.354028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:36.647629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:40.010115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:42.356266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:44.725950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:47.185734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:50.145801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:52.691797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:55.184877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:57.418112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:59.659422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:02.537434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:04.662300image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:06.922292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:32.230219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:34.492307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:36.776143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:40.172072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:42.477528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:44.864480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:47.319295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:50.342730image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:52.854367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:55.344036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:57.538202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:59.795911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:02.680462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:04.806520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:07.042319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:32.371037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:34.647443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:38.059208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:40.307623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:42.624145image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:45.007749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:47.441887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:50.509766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:53.011460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:55.467268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:57.662410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:59.925905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:02.802026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:04.916622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:07.174277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:32.508203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:34.783813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:38.368584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:40.434013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:42.801073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:45.168745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:47.590259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:50.705891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:53.181925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:55.622108image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:57.806409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:00.122783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:02.932646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:05.056566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:07.456311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:32.648927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:34.922806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:38.576547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:40.590066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:42.995889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:45.375870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-22T11:23:55.770396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:57.941672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:00.281121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:03.063363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:05.188393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:07.661106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:32.843219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:35.071771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:38.734033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:40.728046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:43.188940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:45.546868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:47.891209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:51.045074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:53.484975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:55.919276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:58.089026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:00.456101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:03.192846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:05.316694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:07.819780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:32.989093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:35.249964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:38.860861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:40.880734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:43.347934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:45.721076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:48.028949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:51.200291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:53.677780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:56.065278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:58.294700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:00.618999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:03.340345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:05.457490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:08.005473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:33.155065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:35.407331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:38.982475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:41.043736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-22T11:23:58.490109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:00.769560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-22T11:24:05.617529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:08.163228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:33.302068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-22T11:23:39.103616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-22T11:23:44.107831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:46.583210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:49.174687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:52.031568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:54.564209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:56.847474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:59.049155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:01.388362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:04.110563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:06.213598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:08.826012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:33.871985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:36.142146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:39.617627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:41.868485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:44.246676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:46.725153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:49.567385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:52.190622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:54.731177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:56.979022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:59.224861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:01.545450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:04.237826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:06.361597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:09.000045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:34.045911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:36.290503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:39.730877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:42.018120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:44.389100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:46.848152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:49.792844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:52.334172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:54.888470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:57.105548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:23:59.362482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:01.705676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:04.361114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-22T11:24:06.479601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-22T11:24:25.031792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
chile_interest_rates_CHL_CBchina_inflation_CHL_CBdaily_retail_sales_CHL_CBeurozone_inflation_CHL_CBgdp_chained_volume_CHL_CBheadline_cpi_yoy_CHL_CBimacec_monthly_activity_CHL_CBm1_money_supply_CHL_CBm2_money_supply_CHL_CBm3_money_supply_CHL_CBoil_prices_CHL_CBreal_earning_index_(wages)_CHL_CBreal_exchange_rate_CHL_CBunemployment_rate_CHL_CBus_inflation_CHL_CB
chile_interest_rates_CHL_CB1.000-0.099-0.3060.181-0.4560.659-0.420-0.474-0.450-0.4520.256-0.519-0.428-0.686-0.021
china_inflation_CHL_CB-0.0991.0000.6630.6750.1270.2320.135-0.185-0.193-0.1930.224-0.2470.078-0.1980.714
daily_retail_sales_CHL_CB-0.3060.6631.0000.2480.4710.0130.3150.6590.1080.0980.156NaN0.008NaN0.584
eurozone_inflation_CHL_CB0.1810.6750.2481.0000.1270.1440.1560.0620.0650.0640.3440.2090.0330.1240.807
gdp_chained_volume_CHL_CB-0.4560.1270.4710.1271.000-0.1530.9900.9810.9820.9820.4930.9630.4530.1820.099
headline_cpi_yoy_CHL_CB0.6590.2320.0130.144-0.1531.000-0.111-0.829-0.621-0.6220.152-0.5810.397-0.1960.007
imacec_monthly_activity_CHL_CB-0.4200.1350.3150.1560.990-0.1111.0000.9890.9900.9890.4910.9830.4800.1950.108
m1_money_supply_CHL_CB-0.474-0.1850.6590.0620.981-0.8290.9891.0000.9990.9990.4450.992-0.1210.0020.104
m2_money_supply_CHL_CB-0.450-0.1930.1080.0650.982-0.6210.9900.9991.0001.0000.4510.993-0.1200.0130.102
m3_money_supply_CHL_CB-0.452-0.1930.0980.0640.982-0.6220.9890.9991.0001.0000.4530.994-0.1200.0150.102
oil_prices_CHL_CB0.2560.2240.1560.3440.4930.1520.4910.4450.4510.4531.0000.839-0.208-0.5030.319
real_earning_index_(wages)_CHL_CB-0.519-0.247NaN0.2090.963-0.5810.9830.9920.9930.9940.8391.0000.3800.3860.277
real_exchange_rate_CHL_CB-0.4280.0780.0080.0330.4530.3970.480-0.121-0.120-0.120-0.2080.3801.0000.3490.050
unemployment_rate_CHL_CB-0.686-0.198NaN0.1240.182-0.1960.1950.0020.0130.015-0.5030.3860.3491.0000.214
us_inflation_CHL_CB-0.0210.7140.5840.8070.0990.0070.1080.1040.1020.1020.3190.2770.0500.2141.000

Missing values

2023-05-22T11:24:09.287908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-22T11:24:09.720222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-05-22T11:24:10.149636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Datechile_interest_rates_CHL_CBchina_inflation_CHL_CBdaily_retail_sales_CHL_CBeurozone_inflation_CHL_CBgdp_chained_volume_CHL_CBheadline_cpi_yoy_CHL_CBimacec_monthly_activity_CHL_CBinflation_expectations_CHL_CBm1_money_supply_CHL_CBm2_money_supply_CHL_CBm3_money_supply_CHL_CBoil_prices_CHL_CBreal_earning_index_(wages)_CHL_CBreal_exchange_rate_CHL_CBunemployment_rate_CHL_CBus_inflation_CHL_CB
01928-03-01NaNNaNNaNNaNNaN4.526516e-07NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
11928-04-01NaNNaNNaNNaNNaN4.617559e-07NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
21928-05-01NaNNaNNaNNaNNaN4.799945e-07NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
31928-06-01NaNNaNNaNNaNNaN4.890816e-07NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
41928-07-01NaNNaNNaNNaNNaN4.890999e-07NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
51928-08-01NaNNaNNaNNaNNaN4.886226e-07NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
61928-09-01NaNNaNNaNNaNNaN5.255657e-07NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
71928-10-01NaNNaNNaNNaNNaN4.983095e-07NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
81928-11-01NaNNaNNaNNaNNaN4.986300e-07NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
91928-12-01NaNNaNNaNNaNNaN4.892538e-07NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
Datechile_interest_rates_CHL_CBchina_inflation_CHL_CBdaily_retail_sales_CHL_CBeurozone_inflation_CHL_CBgdp_chained_volume_CHL_CBheadline_cpi_yoy_CHL_CBimacec_monthly_activity_CHL_CBinflation_expectations_CHL_CBm1_money_supply_CHL_CBm2_money_supply_CHL_CBm3_money_supply_CHL_CBoil_prices_CHL_CBreal_earning_index_(wages)_CHL_CBreal_exchange_rate_CHL_CBunemployment_rate_CHL_CBus_inflation_CHL_CB
11332022-08-019.752.3-6.28731243.4NaN14.1108.1244283.061025.337558178208.278309312922.66125498.60NaN112.466292NaN15.48
11342022-09-0110.550.9-7.14564741.8NaN13.7107.866148NaN60414.933520180477.310337311942.20910290.16NaN111.359434NaN13.67
11352022-10-0111.07-1.3-3.94728830.453588.91391412.8108.168018NaN58695.154275181438.880892313310.18932093.13NaN113.397834NaN10.23
11362022-11-0111.25-1.3-8.40729926.9NaN13.3107.1437173.056878.051434181072.628494313602.01439291.07NaN108.251076NaN8.17
11372022-12-0111.25-0.7-7.28298424.5NaN12.8107.034111NaN57838.778460183319.590991313092.77191980.90NaN104.809544NaN6.87
11382023-01-0111.25-0.8-4.91970315.1NaN12.3108.813137NaN56886.542870184464.453354313622.41830183.09NaN100.303406NaN5.58
11392023-02-0111.25-1.4-3.96938713.3NaN11.9108.362164NaN55924.828987183693.357748313477.22605282.71NaN96.575178NaN2.39
11402023-03-0111.25-2.5-6.7794115.9NaN11.1108.2680693.055658.340408187269.715174316633.93156178.53NaN96.451197NaN-1.15
11412023-04-0111.25NaNNaNNaNNaN9.9NaNNaN55688.394191188783.955830319009.99098484.11NaNNaNNaNNaN
11422023-05-01NaNNaNNaNNaNNaNNaNNaN3.0NaNNaNNaNNaNNaNNaNNaNNaN